A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise

Deep-Learning-Based System for Change Detection Onboard Earth Observation Small Satellites

In recent years, the important evolution in the number, potentiality, and diversity of Earth observation (EO) satellites has resulted in dramatic increases in the payload data volume and rate. However, these exponential increases in the generated data volume are creating a significant bottleneck onboard EO satellites due to transmission bandwidth limits and communication delays. Onboard imaging payload data processing can provide an appropriate solution to alleviate the induced data bottleneck. It can also facilitate rapid response for decision-making operations. Change detection is one of the most significant functions in onboard payload data processing systems that enable a real-time reaction to natural disasters, such as flooding, earthquakes, and volcanic eruptions. In this article, we address the problem of automatic change detection onboard EO satellites. This article aims to design an automatic onboard change detection system (OCDS) that can run on existing flight-proven hardware by taking advantage of the attractive features of a leading model in deep learning (DL) called convolutional neural network. The contribution of this article is twofold. An efficient algorithmic solution for change detection based on DL that fulfills space environment-induced constraints is first proposed. Second, a preliminary hardware architecture of the proposed OCDS is designed based on payload data processing flight-proven hardware. The experimental results demonstrate the efficiency of the proposed DL-based change detection approach and the suitability of the designed OCDS for onboarding on EO small satellites

Evaluation and assessment of rigid, affine, and nonrigid registration techniques: application to X-ray angiography images

ISBN: 1-60021-992-

An Improved Image Encryption Algorithm Based on Cyclic Rotations and Multiple Chaotic Sequences: Application to Satellite Images

In this paper, a new satellite image encryption algorithm based on the combination of multiple chaotic systems and a random cyclic rotation technique is proposed. Our contribution consists in implementing three different chaotic maps (logistic, sine, and standard) combined to improve the security of satellite images. Besides enhancing the encryption, the proposed algorithm also focuses on advanced efficiency of the ciphered images. Compared with classical encryption schemes based on multiple chaotic maps and the Rubik's cube rotation, our approach has not only the same merits of chaos systems like high sensitivity to initial values, unpredictability, and pseudo-randomness, but also other advantages like a higher number of permutations, better performances in Peak Signal to Noise Ratio (PSNR) and a Maximum Deviation (MD)

A Feature-Based Approach to Automated Registration of Remotely Sensed Images

International audienceIn this paper, a new automatic control point selection and matching technique for satellite image registration is proposed. This technique exploits the invariant relations between regions of a reference and a sensed image, respectively. It involves an edge-based selection of the most distinctive control points in the reference image. The search for the corresponding control points in the sensed image is based on local similarity detection by means of template matching according to a combined invariants-based similarity measure. The final transformation of the sensed image according to the selected control points is performed by using the thin-plate spline (TPS) interpolation. The proposed technique has been successfully applied to register multitemporal SPOT images from urban and agricultural areas. The experimental results demonstrate the efficiency and accuracy of the algorithm which have outperformed manual registration in terms of root mean square error at the control points

An invariant approach for automated image registration of multitemporal remotely sensed imagery,

International audiencen this paper, a new automatic control point selection and matching technique for satellite image registration is proposed. The characteristic of this approach is that it uses features based on image moments and invariant to symmetric blur, scaling, translation, and rotation to establish correspondences between matched regions from two multitemporal images. The automatic extraction of control points is based on an edge detection approach and on local similarity detection by means of template matching according to a combined invariants-based similarity measure. The final transformation of the sensed image according to the selected control points is performed by using the thin-plate spline (TPS) interpolation. The proposed technique has been successfully applied to register multitemporal SPOT images from urban and agricultural areas. The experimental results demonstrate the efficiency and accuracy of the algorithm which have outperformed manual registration in terms of root mean square error at the control points

Feature Based Registration of Satellite Images

International audienc